Improvements of slap swarm algorithm based on dynamic model
Focusing on the problems of unclear parameter meaning and uncertain convergence in salp swarm algorithm(SSA),the differential dynamics model of SSA is constructed,including leader selection mechanism,leader walking mechanism,and follower partial order learning mechanism.The convergence of the system is analyzed with emphasis on the follower partial order learning mechanism.The sufficient conditions for the global optimal convergence of the algorithm are proposed for the leader-walking mechanism and the follower partial order learning mechanism.Based on the analytical result of dynamic behavior,an improved method of heterogeneous steady following rate and partial order multi-drive mechanism of salp swarm is proposed.Only the structure and parameters of the algorithm are adjusted,and the performance of the algorithm is improved without increasing the amount of calculation.The effectiveness of the proposed algorithm is verified by simulation analysis.
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